An analog signal may be continuous in both value and time. That is, an analog signal may be associated with a value (e.g., amplitude) for every instant in time. Moreover, an analog signal may take on any value from an infinite set of possible values (e.g., all real numbers  or all real  numbers within a limited range). By contrast, a digital signal may be a discrete-time sequence of values from a finite set of values.
Thus, in order to convert an analog signal to a digital signal, the analog signal may be sampled at a certain sampling rate fs. For example, the value (e.g., amplitude) of the analog signal may be determined at each uniformly placed sampling interval
  T  =      1          f      s      (e.g., every x microseconds). Converting an analog signal to a digital signal may further include encoding each sample of the analog signal. Specifically, a quantizer (e.g., scalar, vector) may be applied to map the value of each sample from an infinite set of values to a finite set of values.
Analog to digital conversion may introduce distortion that prevents perfect or alias-free reconstruction of an original analog signal from its digital representation. According to the Shannon-Nyquist sampling theorem, the fidelity of a reconstructed analog signal relative to the original analog signal may depend on the sampling rate applied during analog-to-digital conversion. In particular, the theorem proposes that a bandlimited analog signal may be perfectly reconstructed from uniform samples of the signal taken at a sampling rate at or above the Nyquist rate fNy, where fNy is twice the bandwidth B of the analog signal. As such, conventional analog-to-digital converters are typically configured to avoid distortion by sampling at or above the Nyquist rate fNy=2B.